Shoppers are becoming more willing to share personal information in exchange for relevant and timely information regarding the products they desire. Accordingly, marketers are shifting their resource allocation from traditional cost-per-thousand (“CPM”) and awareness-based advertising activities to a cost-per-acquisition or lead model. Marketers are also implementing robust measurement and tracking systems that can use the now-available data streams directed toward maximizing return on investment.
Manufacturers and other retail-based marketers currently utilize programs and operational processes to acquire retail prospects, or leads, and connect these leads directly with retailers, dealers or the like.
A retailer's success is usually measured by determining its categorical close rates that were achieved from a specific set of active leads. Close rates are typically calculated according to a client-driven set of criteria such as by lead source, by brand, by time period, by geographic unit, by retailer or the like. Assessing performance levels using close rates alone can be misleading, however, because close rates do not account for differing expectations based upon the quality of the leads in consideration. For example, if there are major quality differences in a set of leads, then an increase or decrease in close rate does not necessarily reflect follow-up or handling quality by the dealers. Rather, the close rate may be a result of the variable quality of the leads.
As such, a manufacturer or marketer can have a difficult time establishing which close rate should be expected and used as a benchmark for evaluating dealer performance. Assessing the ability of a retailer to extract maximum value from their leads is an important step in maximizing the efficiency of the overall lead management system. Implementing a process to accurately predict and track performance may ensure that appropriate resources are allocated and that the maximum value is extracted from any enterprise lead solution.